2019 21st International Conference on Transparent Optical Networks (ICTON)

Producción Científica5G technology will provide networks with high-bandwidth, low latency and multitenancy. The integration of computing and storage resources in the edge of the fronthaul network, i.e., multi-access edge computing (MEC), will allow to instantiate some virtual network functions (VNF) in those computing resources. The backhaul of 5G networks will be based on optical technology, in particular WDM, due to its high capacity and flexibility. In this paper, we analyse the problem of VNF-provisioning in a metro ring-topology network equipped with MEC resources and with a WDM network connecting the edge nodes. In contrast to previous proposals, the method decides where VNFs must be instantiated but also the design of the virtual topology for the WDM metro network in order to reduce the service blocking ratio and the number of resources in operation.Ministerio de Economía, Industria y Competitividad (project TEC2017-84423-C3-1-P)Ministerio de Industria, Energía y Turismo (project BES-2015-074514

Genetic algorithm for holistic VNF-mapping and virtual topology design

Producción CientíficaNext generation of Internet of Things (IoT) services imposes stringent requirements to the future networks that current ones cannot fulfill. 5G is a technology born to give response to those requirements. However, the deployment of 5G is also accompanied by profound architectural changes in the network, including the introduction of technologies like multi-access edge computing (MEC), software defined networking (SDN), and network function virtualization (NFV). In particular, NFV poses diverse challenges like virtual network function (VNF) placement and chaining, also called VNF-mapping. In this paper, we present an algorithm that solves VNF-placement and chaining in a metro WDM optical network equipped with MEC resources. Therefore, it solves the VNF-mapping in conjunction with the virtual topology design of the underlying optical backhaul network. Moreover, a version of the method providing protection against node failures is also presented. A simulation study is presented to show the importance of designing the three problems jointly, in contrast to other proposals of the literature that do not take the design of the underlying network into consideration when solving that problem. Furthermore, this paper also shows the advantages of using collaboration between MEC nodes to solve the VNF-mapping problem and the advantage of using shared protection schemes. The new algorithm outperforms other proposals in terms of both service blocking ratio, and number of active CPUs (thus reducing energy consumption). Finally, the impact of deploying different physical topologies for the optical backhaul network is also presented.Ministerio de Economía, Industria y Competitividad (grant TEC2017-84423-C3-1-P)Ministerio de Industria, Comercio y Turismo (grant BES 2015-074514)Spanish Thematic Network (contract RED2018-102585-T)INTERREG V-A España-Portugal (POCTEP) program (project 0677_DISRUPTIVE_2_E

CSCI 2017

Producción CientíficaElastic Optical Networks (EON) are seen as an essential technology to implement the backhaul of Future Internet allowing the deployment of emerging paradigms like the Internet of Things (IoT), Tactile Internet, or Industry 4.0. When designing those Elastic Optical Networks (EONs), the Routing and Spectrum Assignment (RSA) problem has to be solved. In this paper, we analyse the effect of using two types of flexibility in a well‑known RSA method. A simulation study will be presented with the main results of this comparison.Ministerio de Economía, Industria y Competitividad (Projects TEC2014-53071-C3-2-P, TEC2015-71932-REDT)Ministerio de Educación, Cultura y Deporte (Proyect BES-2015-074514

11ª Reunión Española de Optoelectrónica, OPTOEL’19

Producción CientíficaPassive Optical Networks (PONs) are the most deployed network infrastructure in the access segment. Indeed, the number of Fibre-to-the-Home (FTTH) subscribers in Europe increased with more than 59.6 million in September 2018. On the other hand, the automation of the PON configuration to lead to better control of the network management may provide many advantages to exploit the PON capabilities, for example to facilitate the integration of Software Defined Networking (SDN) strategies in this networks. Indeed, SDN may improve the network efficiency of networks regarding scalability, management and Quality of Service (QoS) performance. Therefore, we propose a network automation system in Python to control and configure a SDN-GPON solution implemented over a real GPON testbed.Ministerio de Ciencia, Innovación y Universidades (project TEC2017-84423-C3-1-P

Joint Core and Spectrum Allocation in Dynamic Optical Networks with ROADMs with No Line Changes

Producción CientíficaFuture metro networks will connect many multiaccess edge computing resources (MEC) working in a coordinating fashion to provide users with cloud computing capabilities with very low latency. That highly distributed computing architecture has to be connected by a network that provides high bandwidth and flexibility. Elastic optical networks (EONs) are currently the best option to perform that task. In a next step of optical network evolution, EONs can increase the bandwidth that they provide by using multicore fibers (MCF). When dynamic optical circuits are established in these networks, the routing, core and spectrum assignment (RCSA) problem must be solved. In this paper, two algorithms are presented in order to solve the RCSA problem considering continuity constraints in both the spectrum and the core (as we consider a cost-effective metro network architecture based on ROADMs without line changes). One of these versions explores the full spectrum of all cores in order to grant the best solution when solving the RCSA problem. The results of a simulation study show that exploring all the cores when solving the RCSA problem can reduce the blocking ratio of those networks and, therefore, increase its performance at the expense of a slight increment of the computing time required to provide a solution.Ministerio de Economía, Industria y Competitividad. Project ONOFRE-2 (TEC2017-84423-C3-1-P) and the research network Go2Edge (RED2018-102585-T)EU H2020 MSCA Programme under grant agreement no. 953442European Regional Development Fund (ERDF) through the project DISRUPTIVE of the cooperation programme Interreg V-A Spain-Portugal (POCTEP) 2014-2020

A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems

Producción CientíficaIn recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42, PID2021-124463OBI00 y RED2018-102585-T, financiados por MCIN/AEI/10.13039/501100011033

ICTON 2018

Producción CientíficaA genetic algorithm is proposed to map virtual network functions in computing resources over 5G networks with an optical backhauling system. The algorithm outperforms other proposals in terms of blocking ratio and active CPU cores utilization.Ministerio de Economía, Industria y Competitividad (Projects TEC2014- 53071-C3 -2-P, TEC2017-84423- C3 -1-P, TEC2015-71932- REDT)Ministerio de Educación, Cultura y Deporte (Proyects BES -2015-074514 and FPU14/04227

Longitudinal dynamics of SARS-CoV-2-specific cellular and humoral immunity after natural infection or BNT162b2 vaccination

The timing of the development of specific adaptive immunity after natural SARS-CoV-2 infection, and its relevance in clinical outcome, has not been characterized in depth. Description of the long-term maintenance of both cellular and humoral responses elicited by real-world anti-SARS-CoV-2 vaccination is still scarce. Here we aimed to understand the development of optimal protective responses after SARS-CoV-2 infection and vaccination. We performed an early, longitudinal study of S1-, M- and N-specific IFN-γ and IL-2 T cell immunity and anti-S total and neutralizing antibodies in 88 mild, moderate or severe acute COVID-19 patients. Moreover, SARS-CoV-2-specific adaptive immunity was also analysed in 234 COVID-19 recovered subjects, 28 uninfected BNT162b2-vaccinees and 30 uninfected healthy controls. Upon natural infection, cellular and humoral responses were early and coordinated in mild patients, while weak and inconsistent in severe patients. The S1-specific cellular response measured at hospital arrival was an independent predictive factor against severity. In COVID-19 recovered patients, four to seven months post-infection, cellular immunity was maintained but antibodies and neutralization capacity declined. Finally, a robust Th1-driven immune response was developed in uninfected BNT162b2-vaccinees. Three months post-vaccination, the cellular response was comparable, while the humoral response was consistently stronger, to that measured in COVID-19 recovered patients. Thus, measurement of both humoral and cellular responses provides information on prognosis and protection from infection, which may add value for individual and public health recommendations

Invasive Fusariosis in Nonneutropenic Patients, Spain, 2000-2015

Invasive fusariosis (IF) is associated with severe neutropenia in patients with concurrent hematologic conditions. We conducted a retrospective observational study to characterize the epidemiology of IF in 18 Spanish hospitals during 2000-2015. In that time, the frequency of IF in nonneutropenic patients increased from 0.08 cases per 100,000 admissions in 2000-2009 to 0.22 cases per 100,000 admissions in 2010-2015. Nonneutropenic IF patients often had nonhematologic conditions, such as chronic cardiac or lung disease, rheumatoid arthritis, history of solid organ transplantation, or localized fusariosis. The 90-day death rate among nonneutropenic patients (28.6%) and patients with resolved neutropenia (38.1%) was similar. However, the death rate among patients with persistent neutropenia (91.3%) was significantly higher. We used a multivariate Cox regression analysis to characterize risk factors for death: persistent neutropenia was the only risk factor for death, regardless of antifungal therapy

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected